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New integrative computational neuroscience center established at MIT’s McGovern Institute

by Jennifer Michalowski |

With the tools of modern neuroscience, researchers can peer into the brain with unprecedented accuracy. Recording devices listen in on the electrical conversations between neurons, picking up the voices of hundreds of cells at a time. Genetic tools allow us to focus on specific types of neurons based on their molecular signatures. Microscopes zoom in to illuminate the brain’s circuitry, capturing thousands of images of elaborately branched dendrites. Functional MRIs detect changes in blood flow to map activity within a person’s brain, generating a complete picture by compiling hundreds of scans.

This deluge of data provides insights into brain function and dynamics at different levels – molecules, cells, circuits, and behavior — but the insights often remain compartmentalized in separate research silos. An innovative new center at MIT’s McGovern Institute aims to leverage them into powerful revelations of the brain’s inner workings.

The K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center will create advanced mathematical models and computational tools to synthesize the deluge of data across scales and advance our understanding of the brain and mental health.

The center, funded by a $24 million donation from philanthropist Lisa Yang and led by McGovern Institute Associate Investigator Ila Fiete, will take a collaborative approach to computational neuroscience, integrating cutting-edge modeling techniques and data from MIT labs to explain brain function at every level, from the molecular to the behavioral.

“Our goal is that sophisticated, truly integrated computational models of the brain will make it possible to identify how ‘control knobs’ such as genes, proteins, chemicals, and environment drive thoughts and behavior, and to make inroads toward urgent unmet needs in understanding and treating brain disorders,” says Fiete, who is also a brain and cognitive sciences professor at MIT.

“Driven by technologies that generate massive amounts of data, we are entering a new era of translational neuroscience research,” says Yang, whose philanthropic investment in MIT research now exceeds $130 million. “I am confident that the multidisciplinary expertise convened by the ICoN center will revolutionize how we synthesize this data and ultimately understand the brain in health and disease.”

Connecting the data

It is impossible to separate the molecules in the brain from their effects on behavior – although those aspects of neuroscience have traditionally been studied independently, by researchers with vastly different expertise. The ICoN Center will eliminate the divides, bringing together neuroscientists and software engineers to deal with all types of data about the brain.

“The center’s highly collaborative structure, which is essential for unifying multiple levels of understanding, will enable us to recruit talented young scientists eager to revolutionize the field of computational neuroscience,” says Robert Desimone, director of the McGovern Institute. “It is our hope that the ICoN Center’s unique research environment will truly demonstrate a new academic research structure that catalyzes bold, creative research.”

To foster interdisciplinary collaboration, every postdoctoral fellow and engineer at the center will work with multiple faculty mentors. In order to attract young scientists and engineers to the field of computational neuroscience, the center will also provide four graduate fellowships to MIT students each year in perpetuity. Interacting closely with three scientific cores, engineers and fellows will develop computational models and technologies for analyzing molecular data, neural circuits, and behavior, such as tools to identify patterns in neural recordings or automate the analysis of human behavior to aid psychiatric diagnoses. These technologies and models will be instrumental in synthesizing data into knowledge and understanding.

Center priorities

In its first five years, the ICoN Center will prioritize four areas of investigation: episodic memory and exploration, including functions like navigation and spatial memory; complex or stereotypical behavior, such as the perseverative behaviors associated with autism and obsessive-compulsive disorder; cognition and attention; and sleep. Models of complex behavior will be created in collaboration with clinicians and researchers at Children’s Hospital of Philadelphia.

The goal, Fiete says, is to model the neuronal interactions that underlie these functions so that researchers can predict what will happen when something changes — when certain neurons become more active or when a genetic mutation is introduced, for example. When paired with experimental data from MIT labs, the center’s models will help explain not just how these circuits work, but also how they are altered by genes, the environment, aging, and disease. These focus areas encompass circuits and behaviors often affected by psychiatric disorders and neurodegeneration, and models will give researchers new opportunities to explore their origins and potential treatment strategies.

“Lisa Yang is focused on helping the scientific community realize its goals in translational research,” says Nergis Mavalvala, dean of the School of Science and the Curtis and Kathleen Marble Professor of Astrophysics. “With her generous support, we can accelerate the pace of research by connecting the data to the delivery of tangible results.”

 

Seven from MIT receive National Institutes of Health awards

by MIT School of Science |

On Oct. 5, the National Institutes of Health announced the names of 106 scientists who have been awarded grants through the High-Risk, High-Reward Research program to advance highly innovative biomedical and behavioral research. Seven of the recipients are MIT faculty members.

The High-Risk, High-Reward Research program catalyzes scientific discovery by supporting research proposals that, due to their inherent risk, may struggle in the traditional peer-review process despite their transformative potential. Program applicants are encouraged to pursue trailblazing ideas in any area of research relevant to the NIH’s mission to advance knowledge and enhance health.

“The science put forward by this cohort is exceptionally novel and creative and is sure to push at the boundaries of what is known,” says NIH Director Francis S. Collins. “These visionary investigators come from a wide breadth of career stages and show that groundbreaking science can happen at any career level given the right opportunity.”

New innovators

Four MIT researchers received New Innovator Awards, which recognize “unusually innovative research from early career investigators.” They are:

  • Pulin Li is a member at the Whitehead Institute for Biomedical Research and an assistant professor in the Department of Biology. Li combines approaches from synthetic biology, developmental biology, biophysics and systems biology to quantitatively understand the genetic circuits underlying cell-cell communication that creates multicellular behaviors.
  • Seychelle Vos, the Robert A. Swanson (1969) Career Development Professor of Life Sciences in the Department of Biology, studies the interplay of gene expression and genome organization. Her work focuses on understanding how large molecular machineries involved in genome organization and gene transcription regulate each others’ function to ultimately determine cell fate and identity.
  • Xiao Wang, the Thomas D. and Virginia Cabot Assistant Professor of Chemistry and a member of the Broad Institute of MIT and Harvard, aims to develop high-resolution and highly-multiplexed molecular imaging methods across multiple scales toward understanding the physical and chemical basis of brain wiring and function.
  • Alison Wendlandt is a Cecil and Ida Green Career Development Assistant Professor of Chemistry. Wendlandt focuses on the development of selective, catalytic reactions using the tools of organic and organometallic synthesis and physical organic chemistry. Mechanistic study plays a central role in the development of these new transformations.

Transformative researchers

Two MIT researchers have received Transformative Research Awards, which “promote cross-cutting, interdisciplinary approaches that could potentially create or challenge existing paradigms.” The recipients are:

  • Manolis Kellis is a professor of computer science at MIT in the area of computational biology, an associate member of the Broad Institute, and a principal investigator with MIT’s Computer Science and Artificial Intelligence Laboratory. He aims to further our understanding of the human genome by computational integration of large-scale functional and comparative genomics datasets.
  • Myriam Heiman is the Latham Family Career Development Associate Professor of Neuroscience in the Department of Brain and Cognitive Sciences and an investigator in the Picower Institute for Learning and Memory. Heiman studies the selective vulnerability and pathophysiology seen in two neurodegenerative diseases of the basal ganglia, Huntington’s disease, and Parkinson’s disease.

Together, Heiman, Kellis and colleagues will launch a five-year investigation to pinpoint what may be going wrong in specific brain cells and to help identify new treatment approaches for amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration with motor neuron disease (FTLD/MND). The project will bring together four labs, including Heiman and Kellis’ labs at MIT, to apply innovative techniques ranging from computational, genomic, and epigenomic analyses of cells from a rich sample of central nervous system tissue, to precision genetic engineering of stem cells and animal models.

Pioneering researchers

  • Polina Anikeeva received a Pioneer Award, which “challenges investigators at all career levels to pursue new research directions and develop groundbreaking, high-impact approaches to a broad area of biomedical, behavioral, or social science.” Anikeeva is an MIT professor of materials science and engineering, a professor of brain and cognitive sciences, and a McGovern Institute for Brain Research associate investigator. She has established a research program that uniquely combines materials synthesis, device fabrication, neurophysiology, and animal models of behavior. Her group carries out projects that understand, invent, and design materials from the level of atoms to functional devices with applications in fundamental neuroscience.

The program is supported by the NIH Common Fund, which oversees programs that pursue major opportunities and gaps throughout the research enterprise that are of great importance to NIH and require collaboration across the agency to succeed. It issues four awards each year: the Pioneer Award, the New Innovator Award, the Transformative Research Award, and the Early Independence Award.

This year, NIH issued 10 Pioneer awards, 64 New Innovator awards, 19 Transformative Research awards (10 general, four ALS-related, and five Covid-19-related), and 13 Early Independence awards for 2021. Funding for the awards comes from the NIH Common Fund, the National Institute of General Medical Sciences, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke.

Artificial networks learn to smell like the brain

by Jennifer Michalowski |

Using machine learning, a computer model can teach itself to smell in just a few minutes. When it does, researchers have found, it builds a neural network that closely mimics the olfactory circuits that animal brains use to process odors.

Animals from fruit flies to humans all use essentially the same strategy to process olfactory information in the brain. But neuroscientists who trained an artificial neural network to take on a simple odor classification task were surprised to see it replicate biology’s strategy so faithfully.

“The algorithm we use has no resemblance to the actual process of evolution,” says Guangyu Robert Yang, an associate investigator at MIT’s McGovern Institute, who led the work as a postdoctoral fellow at Columbia University. The similarities between the artificial and biological systems suggest that the brain’s olfactory network is optimally suited to its task.

Yang and his collaborators, who reported their findings October 6, 2021, in the journal Neuron, say their artificial network will help researchers learn more about the brain’s olfactory circuits. The work also helps demonstrate artificial neural networks’ relevance to neuroscience. “By showing that we can match the architecture [of the biological system] very precisely, I think that gives more confidence that these neural networks can continue to be useful tools for modeling the brain,” says Yang, who is also an assistant professor in MIT’s Departments of Brain and Cognitive Sciences and Electrical Engineering and Computer Science and a member of the Center for Brains, Minds and Machines.

Mapping natural olfactory circuits

For fruit flies, the organism in which the brain’s olfactory circuitry has been best mapped, smell begins in the antennae. Sensory neurons there, each equipped with odor receptors specialized to detect specific scents, transform the binding of odor molecules into electrical activity. When an odor is detected, these neurons, which make up the first layer of the olfactory network, signal to the second-layer: a set of neurons that reside in a part of the brain called the antennal lobe. In the antennal lobe, sensory neurons that share the same receptor converge onto the same second-layer neuron. “They’re very choosy,” Yang says. “They don’t receive any input from neurons expressing other receptors.” Because it has fewer neurons than the first layer, this part of the network is considered a compression layer. These second-layer neurons, in turn, signal to a larger set of neurons in the third layer. Puzzlingly, those connections appear to be random.

For Yang, a computational neuroscientist, and Columbia University graduate student Peter Yiliu Wang, this knowledge of the fly’s olfactory system represented a unique opportunity. Few parts of the brain have been mapped as comprehensively, and that has made it difficult to evaluate how well certain computational models represent the true architecture of neural circuits, they say.

Building an artificial smell network

Neural networks, in which artificial neurons rewire themselves to perform specific tasks, are computational tools inspired by the brain. They can be trained to pick out patterns within complex datasets, making them valuable for speech and image recognition and other forms of artificial intelligence. There are hints that the neural networks that do this best replicate the activity of the nervous system. But, says Wang, who is now a postdoctoral researcher at Stanford University, differently structured networks could generate similar results, and neuroscientists still need to know whether artificial neural networks reflect the actual structure of biological circuits. With comprehensive anatomical data about fruit fly olfactory circuits, he says: “We’re able to ask this question: Can artificial neural networks truly be used to study the brain?”

Collaborating closely with Columbia neuroscientists Richard Axel and Larry Abbott, Yang and Wang constructed a network of artificial neurons comprising an input layer, a compression layer, and an expansion layer—just like the fruit fly olfactory system. They gave it the same number of neurons as the fruit fly system, but no inherent structure: connections between neurons would be rewired as the model learned to classify odors.

The scientists asked the network to assign data representing different odors to categories, and to correctly categorize not just single odors, but also mixtures of odors. This is something that the brain’s olfactory system is uniquely good at, Yang says. If you combine the scents of two different apples, he explains, the brain still smells apple. In contrast, if two photographs of cats are blended pixel by pixel, the brain no longer sees a cat. This ability is just one feature of the brain’s odor-processing circuits, but captures the essence of the system, Yang says.

It took the artificial network only minutes to organize itself. The structure that emerged was stunningly similar to that found in the fruit fly brain. Each neuron in the compression layer received inputs from a particular type of input neuron and connected, seemingly randomly, to multiple neurons in the expansion layer. What’s more, each neuron in the expansion layer receives connections, on average, from six compression-layer neurons—exactly as occurs in the fruit fly brain.

“It could have been one, it could have been 50. It could have been anywhere in between,” Yang says. “Biology finds six, and our network finds about six as well.” Evolution found this organization through random mutation and natural selection; the artificial network found it through standard machine learning algorithms.

The surprising convergence provides strong support that the brain circuits that interpret olfactory information are optimally organized for their task, he says. Now, researchers can use the model to further explore that structure, exploring how the network evolves under different conditions and manipulating the circuitry in ways that cannot be done experimentally.

Dealing with uncertainty

by Jennifer Michalowski |

As we interact with the world, we are constantly presented with information that is unreliable or incomplete – from jumbled voices in a crowded room to solicitous strangers with unknown motivations. Fortunately, our brains are well equipped to evaluate the quality of the evidence we use to make decisions, usually allowing us to act deliberately, without jumping to conclusions.

Now, neuroscientists at MIT’s McGovern Institute have homed in on key brain circuits that help guide decision-making under conditions of uncertainty. By studying how mice interpret ambiguous sensory cues, they’ve found neurons that stop the brain from using unreliable information.

“One area cares about the content of the message—that’s the prefrontal cortex—and the thalamus seems to care about how certain the input is.” – Michael Halassa

The findings, published October 6, 2021, in the journal Nature, could help researchers develop treatments for schizophrenia and related conditions, whose symptoms may be at least partly due to affected individuals’ inability to effectively gauge uncertainty.

Decoding ambiguity

“A lot of cognition is really about handling different types of uncertainty,” says McGovern Associate Investigator Michael Halassa, explaining that we all must use ambiguous information to make inferences about what’s happening in the world. Part of dealing with this ambiguity involves recognizing how confident we can be in our conclusions. And when this process fails, it can dramatically skew our interpretation of the world around us.

“In my mind, schizophrenia spectrum disorders are really disorders of appropriately inferring the causes of events in the world and what other people think,” says Halassa, who is a practicing psychiatrist. Patients with these disorders often develop strong beliefs based on events or signals most people would dismiss as meaningless or irrelevant, he says. They may assume hidden messages are embedded in a garbled audio recording, or worry that laughing strangers are plotting against them. Such things are not impossible—but delusions arise when patients fail to recognize that they are highly unlikely.

Halassa and postdoctoral researcher Arghya Mukherjee wanted to know how healthy brains handle uncertainty, and recent research from other labs provided some clues. Functional brain imaging had shown that when people are asked to study a scene but they aren’t sure what to pay attention to, a part of the brain called the mediodorsal thalamus becomes active. The less guidance people are given for this task, the harder the mediodorsal thalamus works.

The thalamus is a sort of crossroads within the brain, made up of cells that connect distant brain regions to one another. Its mediodorsal region sends signals to the prefrontal cortex, where sensory information is integrated with our goals, desires, and knowledge to guide behavior. Previous work in the Halassa lab showed that the mediodorsal thalamus helps the prefrontal cortex tune in to the right signals during decision-making, adjusting signaling as needed when circumstances change. Intriguingly, this brain region has been found to be less active in people with schizophrenia than it is in others.

group photo of study authors
Study authors (from left to right) Michael Halassa, Arghya Mukherjee, Norman Lam and Ralf Wimmer.

Working with postdoctoral researcher Norman Lam and research scientist Ralf Wimmer, Halassa and Mukherjee designed a set of animal experiments to examine the mediodorsal thalamus’s role in handling uncertainty. Mice were trained to respond to sensory signals according to audio cues that alerted them whether to focus on either light or sound. When the animals were given conflicting cues, it was up to them animal to figure out which one was represented most prominently and act accordingly. The experimenters varied the uncertainty of this task by manipulating the numbers and ratio of the cues.

Division of labor

By manipulating and recording activity in the animals’ brains, the researchers found that the prefrontal cortex got involved every time mice completed this task, but the mediodorsal thalamus was only needed when the animals were given signals that left them uncertain how to behave. There was a simple division of labor within the brain, Halassa says. “One area cares about the content of the message—that’s the prefrontal cortex—and the thalamus seems to care about how certain the input is.”

Within the mediodorsal thalamus, Halassa and Mukherjee found a subset of cells that were especially active when the animals were presented with conflicting sound cues. These neurons, which connect directly to the prefrontal cortex, are inhibitory neurons, capable of dampening downstream signaling. So when they fire, Halassa says, they effectively stop the brain from acting on unreliable information. Cells of a different type were focused on the uncertainty that arises when signaling is sparse. “There’s a dedicated circuitry to integrate evidence across time to extract meaning out of this kind of assessment,” Mukherjee explains.

As Halassa and Mukherjee investigate these circuits more deeply, a priority will be determining whether they are disrupted in people with schizophrenia. To that end, they are now exploring the circuitry in animal models of the disorder. The hope, Mukherjee says, is to eventually target dysfunctional circuits in patients, using noninvasive, focused drug delivery methods currently under development. “We have the genetic identity of these circuits. We know they express specific types of receptors, so we can find drugs that target these receptors,” he says. “Then you can specifically release these drugs in the mediodorsal thalamus to modulate the circuits as a potential therapeutic strategy.”

This work was funded by grants from the National Institute of Mental Health (R01MH107680-05 and R01MH120118-02).

New bionics center established at MIT with $24 million gift

by Jennifer Michalowski |

A deepening understanding of the brain has created unprecedented opportunities to alleviate the challenges posed by disability. Scientists and engineers are taking design cues from biology itself to create revolutionary technologies that restore the function of bodies affected by injury, aging, or disease – from prosthetic limbs that effortlessly navigate tricky terrain to digital nervous systems that move the body after a spinal cord injury.

With the establishment of the new K. Lisa Yang Center for Bionics, MIT is pushing forward the development and deployment of enabling technologies that communicate directly with the nervous system to mitigate a broad range of disabilities. The center’s scientists, clinicians, and engineers will work together to create, test, and disseminate bionic technologies that integrate with both the body and mind.

The center is funded by a $24 million gift to MIT’s McGovern Institute for Brain Research from philanthropist Lisa Yang, a former investment banker committed to advocacy for individuals with visible and invisible disabilities.

Portait of philanthropist Lisa Yang.
Philanthropist Lisa Yang is committed to advocacy for individuals with visible and invisible disabilities. Photo: Caitlin Cunningham

Her previous gifts to MIT have also enabled the establishment of the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience, Hock E. Tan and K. Lisa Yang Center for Autism Research, Y. Eva Tan Professorship in Neurotechnology, and the endowed K. Lisa Yang Post-Baccalaureate Program.

“The K. Lisa Yang Center for Bionics will provide a dynamic hub for scientists, engineers and designers across MIT to work together on revolutionary answers to the challenges of disability,” says MIT President L. Rafael Reif. “With this visionary gift, Lisa Yang is unleashing a powerful collaborative strategy that will have broad impact across a large spectrum of human conditions – and she is sending a bright signal to the world that the lives of individuals who experience disability matter deeply.”

An interdisciplinary approach

To develop prosthetic limbs that move as the brain commands or optical devices that bypass an injured spinal cord to stimulate muscles, bionic developers must integrate knowledge from a diverse array of fields—from robotics and artificial intelligence to surgery, biomechanics, and design. The K. Lisa Yang Center for Bionics will be deeply interdisciplinary, uniting experts from three MIT schools: Science, Engineering, and Architecture and Planning. With clinical and surgical collaborators at Harvard Medical School, the center will ensure that research advances are tested rapidly and reach people in need, including those in traditionally underserved communities.

To support ongoing efforts to move toward a future without disability, the center will also provide four endowed fellowships for MIT graduate students working in bionics or other research areas focused on improving the lives of individuals who experience disability.

“I am thrilled to support MIT on this major research effort to enable powerful new solutions that improve the quality of life for individuals who experience disability,” says Yang. “This new commitment extends my philanthropic investment into the realm of physical disabilities, and I look forward to the center’s positive impact on countless lives, here in the US and abroad.”

The center will be led by Hugh Herr, a professor of media arts and sciences at MIT’s Media Lab, and Ed Boyden, the Y. Eva Tan Professor of Neurotechnology at MIT, a professor of biological engineering, brain and cognitive sciences, and media arts and sciences, and an investigator at MIT’s McGovern Institute and the Howard Hughes Medical Institute.

A double amputee himself, Herr is a pioneer in the development of bionic limbs to improve mobility for those with physical disabilities. “The world profoundly needs relief from the disabilities imposed by today’s nonexistent or broken technologies. We must continually strive towards a technological future in which disability is no longer a common life experience,” says Herr. “I am thrilled that the Yang Center for Bionics will help to measurably improve the human experience for so many.”

Boyden, who is a renowned creator of tools to analyze and control the brain, will play a key role in merging bionics technologies with the nervous system. “The Yang Center for Bionics will be a research center unlike any other in the world,” he says. “A deep understanding of complex biological systems, coupled with rapid advances in human-machine bionic interfaces, mean we will soon have the capability to offer entirely new strategies for individuals who experience disability. It is an honor to be part of the center’s founding team.”

Center priorities

In its first four years, the K. Lisa Yang Center for Bionics will focus on developing and testing three bionic technologies:

  • Digital nervous system: to eliminate movement disorders caused by spinal cord injuries, using computer-controlled muscle activations to control limb movements while simultaneously stimulating spinal cord repair
  • Brain-controlled limb exoskeletons: to assist weak muscles and enable natural movement for people affected by stroke or musculoskeletal disorders
  • Bionic limb reconstruction: to restore natural, brain-controlled movements as well as the sensation of touch and proprioception (awareness of position and movement) from bionic limbs

A fourth priority will be developing a mobile delivery system to ensure patients in medically underserved communities have access to prosthetic limb services. Investigators will field test a system that uses a mobile clinic to conduct the medical imaging needed to design personalized, comfortable prosthetic limbs and to fit the prostheses to patients where they live. Investigators plan to initially bring this mobile delivery system to Sierra Leone, where thousands of people suffered amputations during the country’s 11-year civil war. While the population of persons with amputation continues to increase each year in Sierra Leone, today less than 10% of persons in need benefit from functional prostheses. Through the mobile delivery system, a key center objective is to scale up production and access of functional limb prostheses for Sierra Leoneans in dire need.

Portrait of Lisa Yang, Hugh Herr, Julius Maada Bio, and David Moinina Sengeh (from left to right).
Philanthropist Lisa Yang (far left) and MIT bionics researcher Hugh Herr (second from left) met with Sierra Leone’s President Julius Maada Bio (second from right) and Chief Innovation Officer for the Directorate of Science, Technology and Innovation, David Moinina Sengeh, to discuss the mobile clinic component of the new K. Lisa Yang Center for Bionics at MIT. Photo: David Moinina Sengeh

“The mobile prosthetics service fueled by the K. Lisa Yang Center for Bionics at MIT is an innovative solution to a global problem,” said Julius Maada Bio, President of Sierra Leone. “I am proud that Sierra Leone will be the first site for deploying this state-of-the-art digital design and fabrication process. As leader of a government that promotes innovative technologies and prioritizes human capital development, I am overjoyed that this pilot project will give Sierra Leoneans (especially in rural areas) access to quality limb prostheses and thus improve their quality of life.”

Together, Herr and Boyden will launch research at the bionics center with three other MIT faculty: Assistant Professor of Media Arts and Sciences Canan Dagdeviren, Walter A. Rosenblith Professor of Cognitive Neuroscience Nancy Kanwisher, and David H. Koch (1962) Institute Professor Robert Langer. They will work closely with three clinical collaborators at Harvard Medical School: orthopedic surgeon Marco Ferrone, plastic surgeon Matthew Carty, and Nancy Oriol, Faculty Associate Dean for Community Engagement in Medical Education.

“Lisa Yang and I share a vision for a future in which each and every person in the world has the right to live without a debilitating disability if they so choose,” adds Herr. “The Yang Center will be a potent catalyst for true innovation and impact in the bionics space, and I am overjoyed to work with my colleagues at MIT, and our accomplished clinical partners at Harvard, to make important steps forward to help realize this vision.”

Tracking time in the brain

by Jennifer Michalowski |

By studying how primates mentally measure time, scientists at MIT’s McGovern Institute have discovered that the brain runs an internal clock whose speed is set by prior experience. In new experiences, the brain closely tracks how elapsed time intervals differ from its preset expectation—indicating that for the brain, time is relative.

The findings, reported September 15, 2021, in the journal Neuron, help explain how the brain uses past experience to make predictions—a powerful strategy for navigating a complex and ever-changing world. The research was led by McGovern Investigator Mehrdad Jazayeri, who is working to understand how the brain forms internal models of the world.

Internal clock

Sensory information tells us a lot about our environment, but the brain needs more than data, Jazayeri says. Internal models are vital for understanding the relationships between things, making generalizations, and interpreting and acting on our perceptions. They help us focus on what’s most important and make predictions about our surroundings, as well as the consequences of our actions. “To be efficient in learning about the world and interacting with the world, we need those predictions,” Jazayeri says. When we enter a new grocery store, for example, we don’t have to check every aisle for the peanut butter, because we know it is likely to be near the jam. Likewise, an experienced racquetball player knows how the ball will move when her paddle hits it a certain way.

Jazayeri’s team was interested in how the brain might make predictions about time. Previously, his team showed how neurons in the frontal cortex—a part of the brain involved in planning—can tick off the passage of time like a metronome. By training monkeys to use an eye movement to indicate the duration of time that separated two flashes of light, they found that cells that track time during this task cooperate to form an adjustable internal clock. Those cells generate a pattern of activity that can be drawn out to measure long time intervals or compressed to track shorter ones. The changes in these signal dynamics reflect elapsed time so precisely that by monitoring the right neurons, Jazayeri’s team can determine exactly how fast a monkey’s internal clock is running.

Predictive processing

Nicolas Meirhaeghe, a graduate student in Mehrdad Jazayeri’s lab, studies how we plan and perform movements in the face of uncertainty. He is pictured here as part of the McGovern Institute 20th anniversary “Rising Stars” photo series. Photo: Michael Spencer

For their most recent experiments, graduate student Nicolas Meirhaeghe designed a series of experiments in which the delay between the two flashes of light changed as the monkeys repeated the task. Sometimes the flashes were separated by just a fraction of a second, sometimes the delay was a bit longer. He found that the time-keeping activity pattern in the frontal cortex occurred over different time scales as the monkeys came to expect delays of different durations. As the duration of the delay fluctuated, the brain appeared to take all prior experience into account, setting the clock to measure the average of those times in anticipation of the next interval.

The behavior of the neurons told the researchers that as a monkey waited for a new set of light cues, it already had an expectation about how long the delay would be. To make such a prediction, Meirhaeghe says, “the brain has no choice but to use all the different values that you perceive from your experience, average those out, and use this as the expectation.”

By analyzing neuronal behavior during their experiments, Jazayeri and Meirhaeghe determined that the brain’s signals were not encoding the full time elapsed between light cues, but instead how that time differed from the predicted time. Calculating this prediction error enabled the monkeys to report back how much time had elapsed.

Neuroscientists have suspected that this strategy, known as predictive processing, is widely used by the brain—although until now there has been little evidence of it outside early sensory areas. “You have a lot of stimuli that are coming from the environment, but lots of stimuli are actually predictable,” Meirhaeghe says. “The idea is that your brain is learning through experience patterns in the environment, and is subtracting your expectation from the incoming signal. What the brain actually processes in the end is the result of this subtraction.”

Finally, the researchers investigated the brain’s ability to update its expectations about time. After presenting monkeys with delays within a particular time range, they switched without warning to times that fluctuated within a new range. The brain responded quickly, updating its internal clock. “If you look inside the brain, after about 100 trials the monkeys have already figured out that these statistics have changed,” says Jazayeri.

It took longer, however—as many as 1,000 trials—for the monkeys to change their behavior in response to the change. “It seems like this prediction, and updating the internal model about the statistics of the world, is way faster than our muscles are able to implement,” Jazayeri says. “Our motor system is kind of lagging behind what our cognitive abilities tell us.” This makes sense, he says, because not every change in the environment merits a change in behavior. “You don’t want to be distracted by every small thing that deviates from your prediction. You want to pay attention to things that have a certain level of consistency.”

Single gene linked to repetitive behaviors, drug addiction

by Jill Crittenden |

Making and breaking habits is a prime function of the striatum, a large forebrain region that underlies the cerebral cortex. McGovern researchers have identified a particular gene that controls striatal function as well as repetitive behaviors that are linked to drug addiction vulnerability.

To identify genes involved specifically in striatal functions, MIT Institute Professor Ann Graybiel previously identified genes that are preferentially expressed in striatal neurons. One identified gene encodes CalDAG-GEFI (CDGI), a signaling molecule that effects changes inside of cells in response to extracellular signals that are received by receptors on the cell surface. In a paper to be published in the October issue of Neurobiology of Disease and now available online, Graybiel, along with former Research Scientist Jill Crittenden and collaborators James Surmeier and Shenyu Zhai at the Feinman School of Medicine at Northwestern University, show that CDGI is key for controlling behavioral responses to drugs of abuse and underlying neuronal plasticity (cellular changes induced by experience) in the striatum.

“This paper represents years of intensive research, which paid off in the end by identifying a specific cellular signaling cascade for controlling repetitive behaviors and neuronal plasticity,” says Graybiel, who is also an investigator at the McGovern Institute and a professor of brain and cognitive sciences at MIT.

McGovern Investigator Ann Graybiel (right) with former Research Scientist Jill Crittenden. Photo: Justin Knight

Surprise discovery

To understand the essential roles of CDGI, Crittenden first engineered “knockout” mice that lack the gene encoding CDGI. Then the Graybiel team began looking for abnormalities in the CDGI knockout mice that could be tied to the loss of CDGI’s function.

Initially, they noticed that the rodent ear-tag IDs often fell off in the knockout mice, an observation that ultimately led to the surprise discovery by the Graybiel team and others that CDGI is expressed in blood platelets and is responsible for a bleeding disorder in humans, dogs, and other animals. The CDGI knockout mice were otherwise healthy and seemed just like their “wildtype” brothers and sisters, which did not carry the gene mutation. To figure out the role of CDGI in the brain, the Graybiel team would have to scrutinize the mice more closely.

Challenging the striatum

Both the CDGI knockout and wildtype mice were given an extensive set of behavioral and neurological tests and the CDGI mice showed deficits in two tests designed to challenge the striatum.

In one test, mice must find their way through a maze by relying on egocentric (i.e. self-referential) cues, such as their turning right or turning left, and not competing allocentric (i.e. external) cues, such as going toward a bright poster on the wall. Egocentric cues are thought to be processed by the striatum whereas allocentric cues are thought to rely on the hippocampus.

In a second test of striatal function, mice learned various gait patterns to match different patterns of rungs on their running wheel, a task designed to test the mouse’s ability to learn and remember a motor sequence.

The CDGI mice learned both of these striatal tasks more slowly than their wildtype siblings, suggesting that the CDGI mice might perform normally in general tests of behavior because they are able to compensate for striatal deficits by using other brain regions such as the hippocampus to solve standard tasks.

The team then decided to give the mice a completely different type of test that relies on the striatum. Because the striatum is strongly activated by drugs of abuse, which elevate dopamine and drive motor habits, Crittenden and collaborator Morgane Thomsen (now at the University of Copenhagen) looked to see whether the CDGI knockout mice respond normally to amphetamine and cocaine.

Psychomotor stimulants like cocaine and amphetamine normally induce a mixture of hyperactive behaviors such as pacing and focused repetitive behaviors like skin-picking (also called stereotypy or punding in humans). The researchers found however, that the drug-induced behaviors in the CDGI knockout mice were less varied than the normal mice and consisted of abnormally prolonged stereotypy, as though the mice were unable to switch between behaviors. The researchers were able to map the abnormal behavior to CDGI function in the striatum by showing that the same vulnerability to drug-induced stereotypy was observed in mice that were engineered to delete CDGI in the striatum after birth (“conditional knockouts”), but to otherwise have normal CDGI throughout the body.

Controlling cravings

In addition to exhibiting prolonged, repetitive behaviors, the CDGI knockout mice had a vulnerability to self-administer drugs. Although previous research had shown that treatments that activate the M1 acetylcholine receptor can block cocaine self-administration, the team found that this therapy was ineffective in CDGI knockout mice. Knockouts continued to self-administer cocaine (suggesting increased craving for the drug) at the same rate before and after M1 receptor activation treatment, even though the treatment succeeded with their sibling control mice. The researchers concluded that CDGI is critically important for controlling repetitive behaviors and the ability to stop self-administration of addictive stimulants.

mouse brain images
Brain sections from control mice (left) and mice engineered for deletion of the CDGI gene after birth. The expression of CDGI in the striatum (arrows) grows stronger as mice grow from pups to adulthood in control mice, but is gradually lost in the CDGI engineered mice (“conditional knockouts”). Image courtesy of the researchers

To better understand how CDGI is linked to the M1 receptor at the cellular level, the team turned to slice physiologists, scientists who record the electrical activity of neurons in brain slices. Their recordings showed that striatal neurons from CDGI knockouts fail to undergo the normal, expected electrophysiological changes after receiving treatments that target the M1 receptor. In particular, the neurons of the striatum that function broadly to stop ongoing behaviors, did not integrate cellular signals properly and failed to undergo “long-term potentiation,” a type of neuronal plasticity thought to underlie learning.

The new findings suggest that excessive repetitive movements are controlled by M1 receptor signaling through CDGI in indirect pathway neurons of the striatum, a neuronal subtype that degenerates in Huntington’s disease and is affected by dopamine loss and l-DOPA replacement therapy in Parkinson’s disease.

“The M1 acetylcholine receptor is a target for therapeutic drug development in treating cognitive and behavioral problems in multiple disorders, but progress has been severely hampered by off-target side-effects related to the wide-spread expression of the M1 receptor,” Graybiel explains. “Our findings suggest that CDGI offers the possibility for forebrain-specific targeting of M1 receptor signaling cascades that are of interest for blocking pathologically repetitive and unwanted behaviors that are common to numerous brain disorders including Huntington’s disease, drug addiction, autism, and schizophrenia as well as drug-induced dyskinesias. We hope that this work can help therapeutic development for these major health problems.”

This work was funded by the James W. (1963) and Patricia T. Poitras Fund, the William N. & Bernice E. Bumpus Foundation, the Saks Kavanaugh Foundation, the Simons Foundation, and the National Institute of Health.

New programmable gene editing proteins found outside of CRISPR systems

by Jennifer Michalowski |

Within the last decade, scientists have adapted CRISPR systems from microbes into gene editing technology, a precise and programmable system for modifying DNA. Now, scientists at MIT’s McGovern Institute and the Broad Institute of MIT and Harvard have discovered a new class of programmable DNA modifying systems called OMEGAs (Obligate Mobile Element Guided Activity), which may naturally be involved in shuffling small bits of DNA throughout bacterial genomes.

These ancient DNA-cutting enzymes are guided to their targets by small pieces of RNA. While they originated in bacteria, they have now  been engineered to work in human cells, suggesting they could be useful in the development of gene editing therapies, particularly as they are small (~30% the size of Cas9), making them easier to deliver to cells than bulkier enzymes. The discovery, reported September 9, 2021, in the journal Science, provides evidence that natural RNA-guided enzymes are among the most abundant proteins on earth, pointing toward a vast new area of biology that is poised to drive the next revolution in genome editing technology.

The research was led by McGovern Investigator Feng Zhang, who is the James and Patricia Poitras Professor of Neuroscience at MIT, a Howard Hughes Medical Institute investigator, and a Core Institute Member of the Broad Institute. Zhang’s team has been exploring natural diversity in search of new molecular systems that can be rationally programmed.

“We are super excited about the discovery of these widespread programmable enzymes, which have been hiding under our noses all along,” says Zhang. “These results suggest the tantalizing possibility that there are many more programmable systems that await discovery and development as useful technologies.”

Natural adaptation

Programmable enzymes, particularly those that use an RNA guide, can be rapidly adapted for different uses. For example, CRISPR enzymes naturally use an RNA guide to target viral invaders, but biologists can direct Cas9 to any target by generating their own RNA guide. “It’s so easy to just change a guide sequence and set a new target,” says graduate student and co-first author of the paper, Soumya Kannan. “So one of the broad questions that we’re interested in is trying to see if other natural systems use that same kind of mechanism.”

Zhang lab graduate student Han Altae-Tran, co-author of the Science paper with Soumya Kannan. Photo: Zhang lab

The first hints that OMEGA proteins might be directed by RNA came from the genes for proteins called IscBs. The IscBs are not involved in CRISPR immunity and were not known to associate with RNA, but they looked like small, DNA-cutting enzymes. The team discovered that each IscB had a small RNA encoded nearby and it directed IscB enzymes to cut specific DNA sequences. They named these RNAs “ωRNAs.”

The team’s experiments showed that two other classes of small proteins known as IsrBs and TnpBs, one of the most abundant genes in bacteria, also use ωRNAs that act as guides to direct the cleavage of DNA.

IscB, IsrB, and TnpB are found in mobile genetic elements called transposons. Graduate student Han Altae-Tran, co-first author on the paper, explains that each time these transposons move, they create a new guide RNA, allowing the enzyme they encode to cut somewhere else.

It’s not clear how bacteria benefit from this genomic shuffling—or whether they do at all.  Transposons are often thought of as selfish bits of DNA, concerned only with their own mobility and preservation, Kannan says. But if hosts can “co-opt” these systems and repurpose them, hosts may gain new abilities, as with CRISPR systems which confer adaptive immunity.

“A lot of the things that we have been thinking about may already exist naturally in some capacity,” says Altae-Tran.

IscBs and TnpBs appear to be predecessors of Cas9 and Cas12 CRISPR systems. The team suspects they, along with IsrB, likely gave rise to other RNA-guided enzymes, too—and they are eager to find them. They are curious about the range of functions that might be carried out in nature by RNA-guided enzymes, Kannan says, and suspect evolution likely already took advantage of OMEGA enzymes like IscBs and TnpBs to solve problems that biologists are keen to tackle.

Comparison of Ω (OMEGA) systems with other known RNA-guided systems. In contrast to CRISPR systems, which capture spacer sequences and store them in the locus within the CRISPR array, Ω systems may transpose their loci (or trans-acting loci) into target sequences, converting targets into ωRNA guides. Image courtesy of the researchers.

“A lot of the things that we have been thinking about may already exist naturally in some capacity,” says Altae-Tran. “Natural versions of these types of systems might be a good starting point to adapt for that particular task.”

The team is also interested in tracing the evolution of RNA-guided systems further into the past. “Finding all these new systems sheds light on how RNA-guided systems have evolved, but we don’t know where RNA-guided activity itself comes from,” Altae-Tran says. Understanding those origins, he says, could pave the way to developing even more classes of programmable tools.

This work was made possible with support from the Simons Center for the Social Brain at MIT; National Institutes of Health Intramural Research Program; National Institutes of Health grants 1R01-HG009761 and 1DP1-HL141201; Howard Hughes Medical Institute; Open Philanthropy; G. Harold and Leila Y. Mathers Charitable Foundation; Edward Mallinckrodt, Jr. Foundation; Poitras Center for Psychiatric Disorders Research at MIT; Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT; Yang-Tan Center for Molecular Therapeutics at MIT; Lisa Yang; Phillips family; R. Metcalfe; and J. and P. Poitras.

RNA-targeting enzyme expands the CRISPR toolkit

by Jennifer Michalowski |

Researchers at MIT’s McGovern Institute have discovered a bacterial enzyme that they say could expand scientists’ CRISPR toolkit, making it easy to cut and edit RNA with the kind of precision that, until now, has only been available for DNA editing. The enzyme, called Cas7-11, modifies RNA targets without harming cells, suggesting that in addition to being a valuable research tool, it provides a fertile platform for therapeutic applications.

“This new enzyme is like the Cas9 of RNA,” says McGovern Fellow Omar Abudayyeh, referring to the DNA-cutting CRISPR enzyme that has revolutionized modern biology by making DNA editing fast, inexpensive, and exact. “It creates two precise cuts and doesn’t destroy the cell in the process like other enzymes,” he adds.

Up until now, only one other family of RNA-targeting enzymes, Cas13, has extensively been developed for RNA targeting applications. However, when Cas13 recognizes its target, it shreds any RNAs in the cell, destroying the cell along the way. Like Cas9, Cas7-11 is part of a programmable system; it can be directed at specific RNA targets using a CRISPR guide. Abudayyeh, McGovern fellow Jonathan Gootenberg, and their colleagues discovered Cas7-11 through a deep exploration of the CRISPR systems found in the microbial world. Their findings are reported today in the journal Nature.

Exploring natural diversity

DNA tools in the CRISPR toolkit (red) are approaching capacity, but researchers are now beginning to find new tools to edit RNA (blue). Image: Steven Dixon

Like other CRISPR proteins, Cas7-11 is used by bacteria as a defense mechanism against viruses. After encountering a new virus, bacteria that employ the CRISPR system keep a record of the infection in the form of a small snippet of the pathogen’s genetic material. Should that virus reappear, the CRISPR system is activated, guided by a small piece of RNA to destroy the viral genome and eliminate the infection.

These ancient immune systems are widespread and diverse, with different bacteria deploying different proteins to counter their viral invaders.

“Some target DNA, some target RNA. Some are very efficient in cleaving the target but have some toxicity, and others do not. They introduce different types of cuts, they can differ in specificity—and so on,” says Eugene Koonin, an evolutionary biologist at the National Center for Biotechnology Information.

Abudayyeh, Gootenberg, and Koonin have been scouring genome sequences to learn about the natural diversity of CRISPR systems—and to mine them for potential tools. The idea, Abudayyeh says, is to take advantage of the work that evolution has already done in engineering protein machines.

“We don’t know what we’ll find,” Abudayyeh says, “but let’s just explore and see what’s out there.”

As the team was poring through public databases to examine the components of different bacterial defense systems, a protein from a bacterium that had been isolated from Tokyo Bay caught their attention. Its amino acid sequence indicated that it belonged to a class of CRISPR systems that use large, multiprotein machines to find and cleave their targets. But this protein appeared to have everything it needed to carry out the job on its own. Other known single-protein Cas enzymes, including the Cas9 protein that has been widely adopted for DNA editing, belong to a separate class of CRISPR systems—but Cas7-11 blurs the boundaries of the CRISPR classification system, Koonin says.

The enzyme, which the team eventually named Cas7-11, was attractive from an engineering perspective, because single proteins are easier to deliver to cells and make better tools than their complex counterparts. But its composition also signaled an unexpected evolutionary history. The team found evidence that through evolution, the components of a more complex Cas machine had fused together to make the Cas7-11 protein. Gootenberg equates this to discovering a bat when you had previously assumed that birds are the only animals that fly, thereby recognizing that there are multiple evolutionary paths to flight. “It totally changes the landscape of how these systems are thought about, both functionally and evolutionarily,” he says.

Precision editing

McGovern Fellows Jonathan Gootenberg and Omar Abudayyeh in their lab. Photo: Caitlin Cunningham

When Gootenberg and Abudayyeh produced the Cas7-11 protein in their lab and began experimenting with it, they realized this unusual enzyme offered a powerful means to manipulate and study RNA. When they introduced it into cells along with an RNA guide, it made remarkably precise cuts, snipping its targets while leaving other RNA undisturbed. This meant they could use Cas7-11 to change specific letters in the RNA code, correcting errors introduced by genetic mutations. They were also able to program Cas7-11 to either stabilize or destroy particular RNA molecules inside cells, which gave them the ability to adjust the levels of the proteins encoded by those RNAs.

Abudayyeh and Gootenberg also found that Cas7-11’s ability to cut RNA could be dampened by a protein that appeared likely to also be involved in triggering programmed cell death, suggesting a possible link between CRISPR defense and a more extreme response to infection.

The team showed that a gene therapy vector can deliver the complete Cas7-11 editing system to cells and that Cas7-11 does not compromise cells’ health. They hope that with further development, the enzyme might one day be used to edit disease-causing sequences out of a patient’s RNA so their cells can produce healthy proteins, or to dial down the level of a protein that is doing harm due to genetic disease.

“We think that the unique way that Cas7-11 cuts enables many interesting and diverse applications,” Gootenberg says, noting that no other CRISPR tool cuts RNA so precisely. “It’s yet another great example of how these basic-biology driven explorations can yield new tools for therapeutics and diagnostics,” he adds. “And we’re certainly still just scratching the surface of what’s out there in natural diversity.”

School of Science welcomes new faculty

Anne Trafton |

This fall, MIT welcomes new faculty members — six assistant professors and two tenured professors — to the departments of Biology; Brain and Cognitive Sciences; Chemistry; Earth, Atmospheric and Planetary Sciences; and Physics.

A physicist, Soonwon Choi is interested in dynamical phenomena that occur in strongly interacting quantum many-body systems far from equilibrium and designing their applications for quantum information science. He takes a variety of interdisciplinary approaches from analytic theory and numerical computations to collaborations on experiments with controlled quantum degrees of freedom. Recently, Choi’s research has encompassed studying the phenomenon of a phase transition in the dynamics of quantum entanglement and information, drawing on machine learning to introduce a quantum convolutional neural network that can recognize quantum states associated with a one-dimensional symmetry-protected topological phase, and exploring a range of quantum applications of the nitrogen-vacancy color center of diamond.

After completing his undergraduate study in physics at Caltech in 2012, Choi received his PhD degree in physics from Harvard University in 2018. He then worked as a Miller Postdoctoral Fellow at the University of California at Berkeley before joining the Department of Physics and the Center for Theoretical Physics as an assistant professor in July 2021.

Olivia Corradin investigates how genetic variants contribute to disease. She focuses on non-coding DNA variants — changes in DNA sequence that can alter the regulation of gene expression — to gain insight into pathogenesis. With her novel outside-variant approach, Corradin’s lab singled out a type of brain cell involved in multiple sclerosis, increasing total heritability identified by three- to five-fold. A recipient of the Avenir Award through the NIH Director’s Pioneer Award Program, Corradin also scrutinizes how genetic and epigenetic variation influence susceptibility to substance abuse disorders. These critical insights into multiple sclerosis, opioid use disorder, and other diseases have the potential to improve risk assessment, diagnosis, treatment, and preventative care for patients.

Corradin completed a bachelor’s degree in biochemistry from Marquette University in 2010 and a PhD in genetics from Case Western Reserve University in 2016. A Whitehead Institute Fellow since 2016, she also became an institute member in July 2021. The Department of Biology welcomes Corradin as an assistant professor.

Arlene Fiore seeks to understand processes that control two-way interactions between air pollutants and the climate system, as well as the sensitivity of atmospheric chemistry to different chemical, physical, and biological sources and sinks at scales ranging from urban to global and daily to decadal. Combining chemistry-climate models and observations from ground, airborne, and satellite platforms, Fiore has identified global dimensions to ground-level ozone smog and particulate haze that arise from linkages with the climate system, global atmospheric composition, and the terrestrial biosphere. She also investigates regional meteorology and climate feedbacks due to aerosols versus greenhouse gases, future air pollution responses to climate change, and drivers of atmospheric oxidizing capacity. A new research direction involves using chemistry-climate model ensemble simulations to identify imprints of climate variability on observational records of trace gases in the troposphere.

After earning a bachelor’s degree and PhD from Harvard University, Fiore held a research scientist position at the Geophysical Fluid Dynamics Laboratory and was appointed as an associate professor with tenure at Columbia University in 2011. Over the last decade, she has worked with air and health management partners to develop applications of satellite and other Earth science datasets to address their emerging needs. Fiore’s honors include the American Geophysical Union (AGU) James R. Holton Junior Scientist Award, Presidential Early Career Award for Scientists and Engineers (the highest honor bestowed by the United States government on outstanding scientists and engineers in the early stages of their independent research careers), and AGU’s James B. Macelwane Medal. The Department of Earth, Atmospheric and Planetary Sciences welcomes Fiore as the first Peter H. Stone and Paola Malanotte Stone Professor.

With a background in magnetism, Danna Freedman leverages inorganic chemistry to solve problems in physics. Within this paradigm, she is creating the next generation of materials for quantum information by designing spin-based quantum bits, or qubits, based in molecules. These molecular qubits can be precisely controlled, opening the door for advances in quantum computation, sensing, and more. She also harnesses high pressure to synthesize new emergent materials, exploring the possibilities of intermetallic compounds and solid-state bonding. Among other innovations, Freedman has realized millisecond coherence times in molecular qubits, created a molecular analogue of an NV center featuring optical read-out of spin, and discovered the first iron-bismuth binary compound.

Freedman received her bachelor’s degree from Harvard University and her PhD from the University of California at Berkeley, then conducted postdoctoral research at MIT before joining the faculty at Northwestern University as an assistant professor in 2012, earning an NSF CAREER Award, the Presidential Early Career Award for Scientists and Engineers, the ACS Award in Pure Chemistry, and more. She was promoted to associate professor in 2018 and full professor with tenure in 2020. Freedman returns to MIT as the Frederick George Keyes Professor of Chemistry.

Kristin Knouse PhD ’17 aims to understand how tissues sense and respond to damage, with the goal of developing new approaches for regenerative medicine. She focuses on the mammalian liver — which has the unique ability to completely regenerate itself — to ask how organisms react to organ injury, how certain cells retain the ability to grow and divide while others do not, and what genes regulate this process. Knouse creates innovative tools, such as a genome-wide CRISPR screening within a living mouse, to examine liver regeneration from the level of a single-cell to the whole organism.

Knouse received a bachelor’s degree in biology from Duke University in 2010 and then enrolled in the Harvard and MIT MD-PhD Program, where she earned a PhD through the MIT Department of Biology in 2016 and an MD through the Harvard-MIT Program in Health Sciences and Technology in 2018. In 2018, she established her independent laboratory at the Whitehead Institute for Biomedical Research and was honored with the NIH Director’s Early Independence Award. Knouse joins the Department of Biology and the Koch Institute for Integrative Cancer Research as an assistant professor.

Lina Necib PhD ’17 is an astroparticle physicist exploring the origin of dark matter through a combination of simulations and observational data that correlate the dynamics of dark matter with that of the stars in the Milky Way. She has investigated the local dynamic structures in the solar neighborhood using the Gaia satellite, contributed to building a catalog of local accreted stars using machine learning techniques, and discovered a new stream called Nyx, after the Greek goddess of the night. Necib is interested in employing Gaia in conjunction with other spectroscopic surveys to understand the dark matter profile in the local solar neighborhood, the center of the galaxy, and in dwarf galaxies.

After obtaining a bachelor’s degree in mathematics and physics from Boston University in 2012 and a PhD in theoretical physics from MIT in 2017, Necib was a Sherman Fairchild Fellow at Caltech, a Presidential Fellow at the University of California at Irvine, and a fellow in theoretical astrophysics at Carnegie Observatories. She returns to MIT as an assistant professor in the Department of Physics and a member of the MIT Kavli Institute for Astrophysics and Space Research.

Andrew Vanderburg studies exoplanets, or planets that orbit stars other than the sun. Conducting astronomical observations from Earth as well as space, he develops cutting-edge methods to learn about planets outside of our solar system. Recently, he has leveraged machine learning to optimize searches and identify planets that were missed by previous techniques. With collaborators, he discovered the eighth planet in the Kepler-90 solar system, a Jupiter-like planet with unexpectedly close orbiting planets, and rocky bodies disintegrating near a white dwarf, providing confirmation of a theory that such stars may accumulate debris from their planetary systems.

Vanderburg received a bachelor’s degree in physics and astrophysics from the University of California at Berkeley in 2013 and a PhD in Astronomy from Harvard University in 2017. Afterward, Vanderburg moved to the University of Texas at Austin as a NASA Sagan Postdoctoral Fellow, then to the University of Wisconsin at Madison as a faculty member. He joins MIT as an assistant professor in the Department of Physics and a member of the Kavli Institute for Astrophysics and Space Research.

A computational neuroscientist, Guangyu Robert Yang is interested in connecting artificial neural networks to the actual functions of cognition. His research incorporates computational and biological systems and uses computational modeling to understand the optimization of neural systems which function to accomplish multiple tasks. As a postdoc, Yang applied principles of machine learning to study the evolution and organization of the olfactory system. The neural networks his models generated show important similarities to the biological circuitry, suggesting that the structure of the olfactory system evolved in order to optimally enable the specific tasks needed for odor recognition.

Yang received a bachelor’s degree in physics from Peking University before obtaining a PhD in computational neuroscience at New York University, followed by an internship in software engineering at Google Brain. Before coming to MIT, he conducted postdoctoral research at the Center for Theoretical Neuroscience of Columbia University, where he was a junior fellow at the Simons Society of Fellows. Yang is an assistant professor in the Department of Brain and Cognitive Sciences with a shared appointment in the Department of Electrical Engineering and Computer Science in the School of Engineering and the MIT Schwarzman College of Computing as well as an associate investigator with the McGovern Institute.